Bibliometric Analysis of Occupational Health and Safety Research in the Construction Industry: Worldwide Trends and Key Focal Points (1990-2023)
Bibliographic record
Abstract
In terms of accidents at work and work-related illnesses, the construction sector ranks among the top three. Occupational health and safety (OHS) research is therefore increasingly prevalent in this sector. However, there is a lack of bibliometric analysis carried out on these studies. The aim of this study is to examine, through bibliometric analysis, the research carried out worldwide on accidents in the construction sector and the key points emphasized in these studies. Bibliometrix, an R-based software, was used to analyze the articles included in this study. Accordingly, 48,046 studies were identified in a search of the SCOPUS database using the term "occupational health and safety". The results of this study indicate that the documents cover the time period from 1990 to 2023 and are spread across 187 different sources, including journals, books, book chapters, and conference papers. With an annual growth rate of 3.39%, the average age of documents is 8.27 years. The safety climate and training are key issues in the studies. When examining the data, it can be observed that the majority of publications come from Australia. Within their respective groups, Turkey, the United Kingdom, Malaysia, Italy, Singapore, South Africa, China, Greece, and Indonesia are closely related. Canada and Spain are connected through other groups. The fact that the most cited study comes from Turkey and is one of the top publications indicates the high priority given to OHS in recent years.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.213 | 0.315 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".